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torch.max#

torch.max(input,*,out=None)→Tensor#

Returns the maximum value of all elements in theinput tensor.

Note

The difference betweenmax/min andamax/amin is:

  • amax/amin supports reducing on multiple dimensions,

  • amax/amin does not return indices.

Bothamax/amin evenly distribute gradients between equal valueswhen there are multiple input elements with the same minimum or maximum value.

Formax/min:

  • If reduce over all dimensions(no dim specified), gradients evenly distribute between equallymax/min values.

  • If reduce over one specified axis, only propagate to the indexed element.

Parameters

input (Tensor) – the input tensor.

Keyword Arguments

out (Tensor,optional) – the output tensor.

Example:

>>>a=torch.randn(1,3)>>>atensor([[ 0.6763,  0.7445, -2.2369]])>>>torch.max(a)tensor(0.7445)
torch.max(input,dim,keepdim=False,*,out=None)

Returns a namedtuple(values,indices) wherevalues is the maximumvalue of each row of theinput tensor in the given dimensiondim. Andindices is the index location of each maximum value found(argmax).

Ifkeepdim isTrue, the output tensors are of the same sizeasinput except in the dimensiondim where they are of size 1.Otherwise,dim is squeezed (seetorch.squeeze()), resultingin the output tensors having 1 fewer dimension thaninput.

Note

If there are multiple maximal values in a reduced row thenthe indices of the first maximal value are returned.

Parameters

  • input (Tensor) – the input tensor.

  • dim (int,optional) – the dimension to reduce. If omitted, all dimensions are reduced. ExplicitNone is not supported.

  • keepdim (bool,optional) – whether the output tensor hasdim retained or not. Default:False.

Keyword Arguments

out (tuple,optional) – the result tuple of two output tensors (max, max_indices)

Example:

>>>a=torch.randn(4,4)>>>atensor([[-1.2360, -0.2942, -0.1222,  0.8475],        [ 1.1949, -1.1127, -2.2379, -0.6702],        [ 1.5717, -0.9207,  0.1297, -1.8768],        [-0.6172,  1.0036, -0.6060, -0.2432]])>>>torch.max(a,1)torch.return_types.max(values=tensor([0.8475, 1.1949, 1.5717, 1.0036]), indices=tensor([3, 0, 0, 1]))>>>a=torch.tensor([[1.0,2.0],[3.0,4.0]])>>>a.max(dim=1,keepdim=True)torch.return_types.max(values=tensor([[2.], [4.]]),indices=tensor([[1], [1]]))>>>a.max(dim=1,keepdim=False)torch.return_types.max(values=tensor([2., 4.]),indices=tensor([1, 1]))
torch.max(input,other,*,out=None)→Tensor

Seetorch.maximum().